Computational Methods for Determining Individuality

Srihari, Sargur N.; Su, Chang

doi:10.1007/978-3-540-85303-9_2

Cited by 4 publications

(2 citation statements)

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“…The results of this comparison stage are typically phrased in terms of an identification (implying that the two impressions came from the same finger), an exclusion (implying that two different fingers created the two impressions), or inconclusive (implying that no determination was possible). During most testimony, the decision is not accompanied by qualifications about confidence or difficulty, and virtually all evidence presented in court is by human experts without reference to a computer match or statistical support from a model of the distributions of features (although this may change; see Neumann et al., , ; Neumann, Champod, Yoo, Genessay, & Langenburg, ; Neumann, Evett, & Skerrett, ; Srihari & Su, ; Su & Srihari, ).…”

Section: Introductionmentioning

confidence: 99%

“…Given that information may be available in different spatial scales, one way in which humans may outperform machine identification algorithms is by combining information across spatial scales. Existing computer models tend to rely on level 2 (classical minutiae) features (Champod & Margot, , ; Egli, Champod, & Margot, ; Fang, Srihari, Srinivasan, & Phatak, ; Neumann et al., , ; Srihari & Su, ; Su & Srihari, , , ), though some more recent proprietary AFIS algorithms may use more pixel‐based information.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing Human Expertise Using Computational Metrics of Feature Diagnosticity in a Pattern Matching Task

et al. 2016

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Forensic evidence often involves an evaluation of whether two impressions were made by the same source, such as whether a fingerprint from a crime scene has detail in agreement with an impression taken from a suspect. Human experts currently outperform computer-based comparison systems, but the strength of the evidence exemplified by the observed detail in agreement must be evaluated against the possibility that some other individual may have created the crime scene impression. Therefore, the strongest evidence comes from features in agreement that are also not shared with other impressions from other individuals. We characterize the nature of human expertise by applying two extant metrics to the images used in a fingerprint recognition task and use eye gaze data from experts to both tune and validate the models. The Attention via Information Maximization (AIM) model (Bruce & Tsotsos, 2009) quantifies the rarity of regions in the fingerprints to determine diagnosticity for purposes of excluding alternative sources. The CoVar model (Karklin & Lewicki, 2009) captures relationships between low-level features, mimicking properties of the early visual system. Both models produced classification and generalization performance in the 75%-80% range when classifying where experts tend to look. A validation study using regions identified by the AIM model as diagnostic demonstrates that human experts perform better when given regions of high diagnosticity. The computational nature of the metrics may help guard against wrongful convictions, as well as provide a quantitative measure of the strength of evidence in casework.

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